[논문]고등학교 수학에서 딥러닝 예측을 이용한 통계교육 프로그램 연구

진혜성; 서보억

doi:10.7468/mathedu.2024.63.2.209

[국내논문] 고등학교 수학에서 딥러닝 예측을 이용한 통계교육 프로그램 연구
Research on a statistics education program utilizing deep learning predictions in high school mathematics 원문보기

Journal of the Korean Society of Mathematical Education. Series A. The Mathematical Education, v.63 no.2, 2024년, pp.209 - 231

진혜성 (대전둔산여자고등학교) , 서보억 (충남대학교)

초록
AI-Helper

4차 산업혁명과 인공지능의 발전으로 교육 분야에서 많은 변화가 일어나고 있다. 특히, 인공지능을 기반으로 하는 교육의 중요성이 강조되고 있다. 이러한 흐름에 따라 본 연구에서는 고등학교 수학에서 딥러닝 예측을 이용한 통계교육 프로그램을 개발하고 이러한 통계적 문제해결 과정 중심의 통계교육 프로그램이 고등학생들의 통계적 소양 및 컴퓨팅 사고력에 미치는 영향을 고찰하고자 한다. 먼저, 본 연구에서는 고등학교 수학에 적용할 수 있는 딥러닝 예측을 이용한 통계교육 프로그램을 개발하였고, 이를 실제 수업상황에 적용하여 분석하였다. 분석 결과, 학생들은 자료가 어떤 맥락에서 생성되고 수집되었는지 경험함으로써 맥락에 대한 이해도가 향상되었으며, 다양한 데이터셋을 탐색하고 분석하는 과정에서 자료의 변이성에 대한 이해도가 높아졌고, 자료의 신뢰성을 검증하는 과정에서 자료를 비판적으로 분석하는 능력을 보였다. 통계교육 프로그램이 고등학생들의 컴퓨팅 사고력에 미치는 영향을 분석하고자 대응 표본 t-검정 시행하였고, 수업 전과 후의 컴퓨팅 사고력 (t=-11.657, p<0.001)은 통계적으로 유의한 차이가 있음을 확인하였다.

Abstract ▼ AI-Helper

The education sector is undergoing significant changes due to the Fourth Industrial Revolution and the advancement of artificial intelligence. Particularly, the importance of education based on artificial intelligence is being emphasized. Accordingly, the purpose of this study is to develop a statistics education program using deep learning prediction in high school mathematics and to examine the impact of such statistically problem-solvingcentered statistics education programs on high school students' statistical literacy and computational thinking. To achieve this goal, a statistics education program using deep learning prediction applicable to high school mathematics was developed. The analysis revealed that students' understanding of context improved through experiencing how data was generated and collected. Additionally, they enhanced their comprehension of data variability while exploring and analyzing various datasets. Moreover, they demonstrated the ability to critically analyze data during the process of validating its reliability. In order to analyze the impact of the statistics education program on high school students' computational thinking, a paired sample t-test was conducted, confirming a statistically significant difference in computational thinking between before and after classes (t=-11.657, p<0.001).

Keyword

표/그림 (12)

표 Table 1. Statistical problem solving is an investigative process (Franklin et al., 2007, p.11)
표 Table 2. CT vocabulary and progression chart (CSTA & ISTE, 2011, pp. 8-9)
그림 Figure 1. Research methods and procedures.
표 Table 3. Statistical literacy framework for statistical problem-solving process
표 Table 4. Composition of research tools and examples of survey questions
표 Table 5. Detailed contents for each session of the statistics education program
표 Table 6. The mathematical content covered in a statistics education program utilizing deep learning predictions
표 Table 7. Group inquiry topics
표 Table 8. Analysis results of statistical literacy levels
그림 Figure 2. Infographics using programming packages.
표 Table 9. Pre- and post-inspection results for PSB-CTQ overall and by domain
표 Table 10. Pre- and post-inspection results by subcomponents of computational thinking domain

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